Localization of Anatomical Landmarks in Head CT Images for Image to Patient Registration
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32870
Localization of Anatomical Landmarks in Head CT Images for Image to Patient Registration

Authors: M. Ovinis, D. Kerr, K. Bouazza-Marouf, M. Vloeberghs


The use of anatomical landmarks as a basis for image to patient registration is appealing because the registration may be performed retrospectively. We have previously proposed the use of two anatomical soft tissue landmarks of the head, the canthus (corner of the eye) and the tragus (a small, pointed, cartilaginous flap of the ear), as a registration basis for an automated CT image to patient registration system, and described their localization in patient space using close range photogrammetry. In this paper, the automatic localization of these landmarks in CT images, based on their curvature saliency and using a rule based system that incorporates prior knowledge of their characteristics, is described. Existing approaches to landmark localization in CT images are predominantly semi-automatic and primarily for localizing internal landmarks. To validate our approach, the positions of the landmarks localized automatically and manually in near isotropic CT images of 102 patients were compared. The average difference was 1.2mm (std = 0.9mm, max = 4.5mm) for the medial canthus and 0.8mm (std = 0.6mm, max = 2.6mm) for the tragus. The medial canthus and tragus can be automatically localized in CT images, with performance comparable to manual localization, based on the approach presented.

Keywords: Anatomical Landmarks, CT, Localization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336226

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3296


[1] M. Gooroochurn, D. Kerr, K. Bouazza-Marouf and M. Ovinis. Facial Recognition Techniques Applied to the Automated Registration of Patients in the Emergency Treatment of Head Injuries. P. I. Mech. Eng. H. Med., 225, 2011, pp. 170-180.
[2] S. Frantz, K. Rohr and H. S. Stiehl. Development and Validation of A Multi-Step Approach to Improved Detection of 3D Point Landmarks in Tomographic Images. Image Vision Comput., 23, 2005, pp. 956-971.
[3] Frantz S, Rohr K and Stiehl HS. Localization of 3D Anatomical Point Landmarks in 3D Tomographic Images Using Deformable Models. Lect Notes Comput Sc., 2000. pp. 492-501.
[4] S. Wörz and K. Rohr. Localization of Anatomical Point Landmarks in 3D Medical Images by Fitting 3D Parametric Intensity Models. Med. Image Anal., 10, 2006, pp. 41-58.
[5] K. Subburaj, B. Ravi and M. Agarwal. Automated Identification of Anatomical Landmarks on 3D Bone Models Reconstructed From CT Scan Images. Comput. Med. Imaging Graphics., 33, 2009, pp. 359-368.
[6] D. Deo and D. Sen. Mesh Processing for Computerized Facial Anthropometry. J. Comput. Inf. Sci. Eng., 10, 2010, pp. 1-12.
[7] D. Chen, G. Mamic , C. Fookes , S. Sridharan. Scale-Space Volume Descriptors for Automatic 3D Facial Feature Extraction. Int. J. Signal Processing., 5, 2009, pp. 264-269.
[8] The MathWorks, Source Code for the Matlab Isosurface Function. 2007.
[9] P. Alliez, D. Cohen-Steiner, O. Devillers, B. Lévy and M. Desbrun. Anisotropic Polygonal Remeshing," ACM T. Graphic. 22, pp. 485-493.
[10] M. Ester, H. P. Kriegel, J. Sander and X. Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proc., 2nd Int. Conf. on Knowledge Discovery and Data Mining, 1996, pp. 226-231.
[11] J. Cao, A. Tagliasacchi, M. Olson, H. Zhang and Z. Su. Point Cloud Skeletons via Laplacian Based Contraction. Shape Modeling Int. Conf., 2010, pp. 187-197.
[12] M. Gooroochurn, M. Ovinis, D. Kerr, K. Bouazza-Marouf and M. Vloeberghs. A Registration Framework for Preoperative CT to Intraoperative White Light Images. Medical Image Understanding and Analysis, 2009, pp. 184-188.
[13] J. M. Fitzpatrick and J. B. West. The Distribution of Target Registration Error in Rigid-Body Point-Based Registration. IEEE Trans. Med. Imag., 20, pp.917-927.
[14] A. A. Salah and L. Akarun. 3D Facial Feature Localization for Registration. Lect Notes Comput Sc., 4105, 2006, pp. 338-345.